• International journal of advanced smart convergence
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• 제9권3호
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• 전기학회논문지
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• 제57권4호
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• pp.700-705
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• 2008

In this paper, we propose object classification algorithm for real-time surveillance system. We have approached this problem using silhouette-based template matching. The silhouette of the object is extracted, and then it is compared with representative template models. Template models are previously stored in the database. Our algorithm is similar to previous pixel-based template matching scheme like Hausdorff Distance, but we use 1D image array rather than 2D regions inspired by Hilbert Path. Transformation of images could reduce computational burden to compute similarity between the detected image and the template images. Experimental results show robustness and real-time performance in object classification, even in low resolution images.

• 융합보안논문지
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• 제24권1호
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• pp.77-82
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• 2024

최근 연구를 통해 다양한 물체 탐지 및 분류 모델은 전반적으로 크게 성능 향상이 이루워졌지만, 물체가 부분적으로 노출된 상황에서의 물체 탐지 및 분류에 대한 연구는 미흡한 실정이다. 특히, 군사 분야에서 무인전투체계가 물체를 탐지하고 분류하는 데 사용되는 경우, 군사적 상황에서 물체는 일반적으로 부분적으로 가려진 상태나 위장된 상태일 가능성이 높다. 본 연구에서는 부분적으로 가려진 물체의 분류 성능을 향상시키는 방법을 제안한다. 이 방법은 물체 이미지 상에 특정 부분을 주변 환경을 고려하여 가리는 부분을 추가하여 은·엄폐 및 위장된 물체에 대한 분류 성능을 개선시켰다. 실험결과로 제안 방법을 적용하였을 때 은·엄폐 및 위장된 물체에 대해서 기존 방법에 비해 물체 분류 향상이 있음을 볼 수가 있었다.

• 자동차안전학회지
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• 제12권4호
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• 한국측량학회지
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• 제33권3호
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.123-125
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• 2005

Many researchers conducted the effort for improving the classification accuracy of satellite image. Most of the study has used optical spectrum information of each pixel for image classification. By applying this method for high resolution satellite image, number of class becomes increase. This situation is remarkable for house, because the roof of house has variety of many colors. Even if the classification is carried out for many classes, roof color information of each house is not necessary. Most of the case, we need the information that object is house or not. In this study, we propose the method for detecting the object by using Genetic Algorithms (GA). Aircraft was selected as object. It is easy for this object to detect in the airport. An aircraft was taken as a template. Object image was taken from QuickBird. Target image includes an aircraft and Haneda Airport. Chromosome has four or five parameters which are composed of number of template, position (x,y), rotation angle, rate of enlarge. Good results were obtained in the experiment.

• 한국지리정보학회지
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• 제20권1호
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• pp.1-14
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• 2017

토지피복도는 지역의 현황을 파악하는 기초적 자료이지만 시간적 공간적 해상도의 한계로 인하여 생태 연구 분야에서의 활용성은 떨어지는 측면이 있다. 이에 본 연구에서는 UAV으로 취득된 고해상도 영상을 기반으로 토지피복도 제작과 자료의 활용가능성을 알아보고자 하였다. UAV를 이용하여 연구대상지 $2.5km^2$ 범위에서 10.5cm 정사영상을 취득하였으며 객체기반(Object-based)과 화소기반(pixel-based) 분류를 통해 얻어진 토지피복도를 비교 분석하였다. 정확도 검증 결과 화소기반 분류는 Kappa 0.77, 객체기반 분류는 Kappa 0.82로 분류정확도가 높았으며, 전반적인 면적비율은 유사하지만 초지, 습지 지역에서 양호한 분류 결과가 나타났다. 객체기반 분류를 위한 최적의 영상분할 가중치는 Scale150, Shape 0.5, Compactness 0.5, Color 1로 선정하였으며 가중치 선정과정에서 Scale이 가장 큰 영향을 주었다. 화소기반 분류 결과와 비교해 객체간의 명확한 경계를 가지므로 결과물 판독이 용이한 것으로 나타났으며, 환경부 토지피복도(세분류)와 비교하여 개발지역(도로, 건물 등)을 제외한 자연지역(산림, 초지, 습지 등)의 분류에 효과적이었다. UAV 영상을 활용한 토지피복 분류방법으로서 객체기반 분류기법의 적용은 자료의 최신성, 정확성, 경제성 등의 장점으로 생태 연구 분야에 기여할 수 있을 것으로 판단된다.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.125-127
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• 2004

High-resolution satellite images offer abundance information of the earth surface for remote sensing applications. The information includes geometry, texture and attribute characteristic. The pixel-based image classification can't satisfy high-resolution satellite image's classification precision and produce large data redundancy. Object-oriented information extraction not only depends on spectrum character, but also use geometry and structure information. It can provide an accessible and truly revolutionary approach. Using Beijing Spot 5 high-resolution image and object-oriented classification with the eCognition software, we accomplish the cultures' precise classification. The test areas have five culture types including water, vegetation, road, building and bare lands. We use nearest neighbor classification and appraise the overall classification accuracy. The average of five species reaches 0.90. All of maximum is 1. The standard deviation is less than 0.11. The overall accuracy can reach $95.47\%.$ This method offers a new technology for high-resolution satellite images' available applications in remote sensing culture classification.

• 대한원격탐사학회지
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• 제40권1호
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.